Merge branch 'main' of github.com:keras-team/keras-core

guides/distributed_training_with_jax.py

@@ -58,9 +58,9 @@ def get_model():
     # Make a simple convnet with batch normalization and dropout.
     inputs = keras.Input(shape=(28, 28, 1))
     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)
-    x = keras.layers.Conv2D(filters=12, kernel_size=3, padding="same", use_bias=False)(
-        x
-    )
+    x = keras.layers.Conv2D(
+        filters=12, kernel_size=3, padding="same", use_bias=False
+    )(x)
     x = keras.layers.BatchNormalization(scale=False, center=True)(x)
     x = keras.layers.ReLU()(x)
     x = keras.layers.Conv2D(
@@ -187,7 +187,11 @@ compute_gradients = jax.value_and_grad(compute_loss, has_aux=True)
 # Training step, Keras provides a pure functional optimizer.stateless_apply
 @jax.jit
 def train_step(train_state, x, y):
-    trainable_variables, non_trainable_variables, optimizer_variables = train_state
+    (
+        trainable_variables,
+        non_trainable_variables,
+        optimizer_variables,
+    ) = train_state
     (loss_value, non_trainable_variables), grads = compute_gradients(
         trainable_variables, non_trainable_variables, x, y
     )
@@ -211,7 +215,9 @@ def get_replicated_train_state(devices):
     var_replication = NamedSharding(var_mesh, P())
 
     # Apply the distribution settings to the model variables
-    trainable_variables = jax.device_put(model.trainable_variables, var_replication)
+    trainable_variables = jax.device_put(
+        model.trainable_variables, var_replication
+    )
     non_trainable_variables = jax.device_put(
         model.non_trainable_variables, var_replication
     )
@@ -255,7 +261,9 @@ for epoch in range(num_epochs):
 trainable_variables, non_trainable_variables, optimizer_variables = train_state
 for variable, value in zip(model.trainable_variables, trainable_variables):
     variable.assign(value)
-for variable, value in zip(model.non_trainable_variables, non_trainable_variables):
+for variable, value in zip(
+    model.non_trainable_variables, non_trainable_variables
+):
     variable.assign(value)
 
 """

guides/distributed_training_with_torch.py

@@ -53,9 +53,9 @@ def get_model():
     # Make a simple convnet with batch normalization and dropout.
     inputs = keras.Input(shape=(28, 28, 1))
     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)
-    x = keras.layers.Conv2D(filters=12, kernel_size=3, padding="same", use_bias=False)(
-        x
-    )
+    x = keras.layers.Conv2D(
+        filters=12, kernel_size=3, padding="same", use_bias=False
+    )(x)
     x = keras.layers.BatchNormalization(scale=False, center=True)(x)
     x = keras.layers.ReLU()(x)
     x = keras.layers.Conv2D(
@@ -231,7 +231,9 @@ def per_device_launch_fn(current_gpu_index, num_gpu):
     model = get_model()
 
     # prepare the dataloader
-    dataloader = prepare_dataloader(dataset, current_gpu_index, num_gpu, batch_size)
+    dataloader = prepare_dataloader(
+        dataset, current_gpu_index, num_gpu, batch_size
+    )
 
     # Instantiate the torch optimizer
     optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

keras_core/backend/jax/numpy.py

@@ -107,10 +107,18 @@ def arccos(x):
     return jnp.arccos(x)
 
 
+def arccosh(x):
+    return jnp.arccosh(x)
+
+
 def arcsin(x):
     return jnp.arcsin(x)
 
 
+def arcsinh(x):
+    return jnp.arcsinh(x)
+
+
 def arctan(x):
     return jnp.arctan(x)
 
@@ -119,6 +127,10 @@ def arctan2(x1, x2):
     return jnp.arctan2(x1, x2)
 
 
+def arctanh(x):
+    return jnp.arctanh(x)
+
+
 def argmax(x, axis=None):
     return jnp.argmax(x, axis=axis)
 
@@ -171,6 +183,10 @@ def cos(x):
     return jnp.cos(x)
 
 
+def cosh(x):
+    return jnp.cosh(x)
+
+
 def count_nonzero(x, axis=None):
     return jnp.count_nonzero(x, axis=axis)
 
@@ -441,6 +457,10 @@ def sin(x):
     return jnp.sin(x)
 
 
+def sinh(x):
+    return jnp.sinh(x)
+
+
 def size(x):
     return jnp.size(x)
 
@@ -479,6 +499,10 @@ def tan(x):
     return jnp.tan(x)
 
 
+def tanh(x):
+    return jnp.tanh(x)
+
+
 def tensordot(x1, x2, axes=2):
     x1 = convert_to_tensor(x1)
     x2 = convert_to_tensor(x2)

keras_core/backend/numpy/numpy.py

@@ -84,10 +84,18 @@ def arccos(x):
     return np.arccos(x)
 
 
+def arccosh(x):
+    return np.arccosh(x)
+
+
 def arcsin(x):
     return np.arcsin(x)
 
 
+def arcsinh(x):
+    return np.arcsinh(x)
+
+
 def arctan(x):
     return np.arctan(x)
 
@@ -96,6 +104,10 @@ def arctan2(x1, x2):
     return np.arctan2(x1, x2)
 
 
+def arctanh(x):
+    return np.arctanh(x)
+
+
 def argmax(x, axis=None):
     axis = tuple(axis) if isinstance(axis, list) else axis
     return np.argmax(x, axis=axis)
@@ -157,6 +169,10 @@ def cos(x):
     return np.cos(x)
 
 
+def cosh(x):
+    return np.cosh(x)
+
+
 def count_nonzero(x, axis=None):
     axis = tuple(axis) if isinstance(axis, list) else axis
     return np.count_nonzero(x, axis=axis)
@@ -438,6 +454,10 @@ def sin(x):
     return np.sin(x)
 
 
+def sinh(x):
+    return np.sinh(x)
+
+
 def size(x):
     return np.size(x)
 
@@ -480,6 +500,10 @@ def tan(x):
     return np.tan(x)
 
 
+def tanh(x):
+    return np.tanh(x)
+
+
 def tensordot(x1, x2, axes=2):
     axes = tuple(axes) if isinstance(axes, list) else axes
     return np.tensordot(x1, x2, axes=axes)

keras_core/backend/tensorflow/numpy.py

@@ -105,10 +105,18 @@ def arccos(x):
     return tfnp.arccos(x)
 
 
+def arccosh(x):
+    return tfnp.arccosh(x)
+
+
 def arcsin(x):
     return tfnp.arcsin(x)
 
 
+def arcsinh(x):
+    return tfnp.arcsinh(x)
+
+
 def arctan(x):
     return tfnp.arctan(x)
 
@@ -117,6 +125,10 @@ def arctan2(x1, x2):
     return tfnp.arctan2(x1, x2)
 
 
+def arctanh(x):
+    return tfnp.arctanh(x)
+
+
 def argmax(x, axis=None):
     return tfnp.argmax(x, axis=axis)
 
@@ -174,6 +186,10 @@ def cos(x):
     return tfnp.cos(x)
 
 
+def cosh(x):
+    return tfnp.cosh(x)
+
+
 def count_nonzero(x, axis=None):
     return tfnp.count_nonzero(x, axis=axis)
 
@@ -472,6 +488,10 @@ def sin(x):
     return tfnp.sin(x)
 
 
+def sinh(x):
+    return tfnp.sinh(x)
+
+
 def size(x):
     return tfnp.size(x)
 
@@ -508,6 +528,10 @@ def tan(x):
     return tfnp.tan(x)
 
 
+def tanh(x):
+    return tfnp.tanh(x)
+
+
 def tensordot(x1, x2, axes=2):
     return tfnp.tensordot(x1, x2, axes=axes)
 

keras_core/backend/torch/numpy.py

@@ -173,11 +173,21 @@ def arccos(x):
     return torch.arccos(x)
 
 
+def arccosh(x):
+    x = convert_to_tensor(x)
+    return torch.arccosh(x)
+
+
 def arcsin(x):
     x = convert_to_tensor(x)
     return torch.arcsin(x)
 
 
+def arcsinh(x):
+    x = convert_to_tensor(x)
+    return torch.arcsinh(x)
+
+
 def arctan(x):
     x = convert_to_tensor(x)
     return torch.arctan(x)
@@ -188,6 +198,11 @@ def arctan2(x1, x2):
     return torch.arctan2(x1, x2)
 
 
+def arctanh(x):
+    x = convert_to_tensor(x)
+    return torch.arctanh(x)
+
+
 def argmax(x, axis=None):
     x = convert_to_tensor(x)
     return torch.argmax(x, dim=axis)
@@ -277,6 +292,11 @@ def cos(x):
     return torch.cos(x)
 
 
+def cosh(x):
+    x = convert_to_tensor(x)
+    return torch.cosh(x)
+
+
 def count_nonzero(x, axis=None):
     x = convert_to_tensor(x)
     if axis == () or axis == []:
@@ -729,6 +749,11 @@ def sin(x):
     return torch.sin(x)
 
 
+def sinh(x):
+    x = convert_to_tensor(x)
+    return torch.sinh(x)
+
+
 def size(x):
     x_shape = convert_to_tensor(tuple(x.shape))
     return torch.prod(x_shape)
@@ -806,6 +831,11 @@ def tan(x):
     return torch.tan(x)
 
 
+def tanh(x):
+    x = convert_to_tensor(x)
+    return torch.tanh(x)
+
+
 def tensordot(x1, x2, axes=2):
     x1, x2 = convert_to_tensor(x1), convert_to_tensor(x2)
     # Conversion to long necessary for `torch.tensordot`

keras_core/ops/nn.py

@@ -57,21 +57,6 @@ def sigmoid(x):
     return backend.nn.sigmoid(x)
 
 
-class Tanh(Operation):
-    def call(self, x):
-        return backend.nn.tanh(x)
-
-    def compute_output_spec(self, x):
-        return KerasTensor(x.shape, dtype=x.dtype)
-
-
-@keras_core_export(["keras_core.ops.tanh", "keras_core.ops.nn.tanh"])
-def tanh(x):
-    if any_symbolic_tensors((x,)):
-        return Tanh().symbolic_call(x)
-    return backend.nn.tanh(x)
-
-
 class Softplus(Operation):
     def call(self, x):
         return backend.nn.softplus(x)

keras_core/ops/numpy.py

@@ -10,9 +10,12 @@ amin
 append
 arange
 arccos
+arccosh
 arcsin
+arcsinh
 arctan
 arctan2
+arctanh
 argmax
 argmin
 argsort
@@ -27,6 +30,7 @@ conj
 conjugate
 copy
 cos
+cosh
 count_nonzero
 cross
 cumprod
@@ -102,6 +106,7 @@ roll
 round
 sign
 sin
+sinh
 size
 sort
 split
@@ -116,6 +121,7 @@ swapaxes
 take
 take_along_axis
 tan
+tanh
 tensordot
 tile
 trace
@@ -713,6 +719,28 @@ def arccos(x):
     return backend.numpy.arccos(x)
 
 
+class Arccosh(Operation):
+    def call(self, x):
+        return backend.numpy.arccosh(x)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=x.dtype)
+
+
+def arccosh(x):
+    """Inverse hyperbolic cosine, element-wise.
+
+    Arguments:
+        x: Input tensor.
+
+    Returns:
+        Output tensor of same shape as x.
+    """
+    if any_symbolic_tensors((x,)):
+        return Arccosh().symbolic_call(x)
+    return backend.numpy.arccosh(x)
+
+
 class Arcsin(Operation):
     def call(self, x):
         return backend.numpy.arcsin(x)
@@ -742,6 +770,29 @@ def arcsin(x):
     return backend.numpy.arcsin(x)
 
 
+class Arcsinh(Operation):
+    def call(self, x):
+        return backend.numpy.arcsinh(x)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=x.dtype)
+
+
+@keras_core_export(["keras_core.ops.arcsinh", "keras_core.ops.numpy.arcsinh"])
+def arcsinh(x):
+    """Inverse hyperbolic sine, element-wise.
+
+    Arguments:
+        x: Input tensor.
+
+    Returns:
+        Output tensor of same shape as x.
+    """
+    if any_symbolic_tensors((x,)):
+        return Arcsinh().symbolic_call(x)
+    return backend.numpy.arcsinh(x)
+
+
 class Arctan(Operation):
     def call(self, x):
         return backend.numpy.arctan(x)
@@ -826,6 +877,29 @@ def arctan2(x1, x2):
     return backend.numpy.arctan2(x1, x2)
 
 
+class Arctanh(Operation):
+    def call(self, x):
+        return backend.numpy.arctanh(x)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=x.dtype)
+
+
+@keras_core_export(["keras_core.ops.arctanh", "keras_core.ops.numpy.arctanh"])
+def arctanh(x):
+    """Inverse hyperbolic tangent, element-wise.
+
+    Arguments:
+        x: Input tensor.
+
+    Returns:
+        Output tensor of same shape as x.
+    """
+    if any_symbolic_tensors((x,)):
+        return Arctanh().symbolic_call(x)
+    return backend.numpy.arctanh(x)
+
+
 class Argmax(Operation):
     def __init__(self, axis=None):
         super().__init__()
@@ -1289,6 +1363,29 @@ def cos(x):
     return backend.numpy.cos(x)
 
 
+class Cosh(Operation):
+    def call(self, x):
+        return backend.numpy.cosh(x)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=x.dtype)
+
+
+@keras_core_export(["keras_core.ops.cosh", "keras_core.ops.numpy.cosh"])
+def cosh(x):
+    """Hyperbolic cosine, element-wise.
+
+    Arguments:
+        x: Input tensor.
+
+    Returns:
+        Output tensor of same shape as x.
+    """
+    if any_symbolic_tensors((x,)):
+        return Cosh().symbolic_call(x)
+    return backend.numpy.cosh(x)
+
+
 class CountNonzero(Operation):
     def __init__(self, axis=None):
         super().__init__()
@@ -3115,6 +3212,29 @@ def sin(x):
     return backend.numpy.sin(x)
 
 
+class Sinh(Operation):
+    def call(self, x):
+        return backend.numpy.sinh(x)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=x.dtype)
+
+
+@keras_core_export(["keras_core.ops.sinh", "keras_core.ops.numpy.sinh"])
+def sinh(x):
+    """Hyperbolic sine, element-wise.
+
+    Arguments:
+        x: Input tensor.
+
+    Returns:
+        Output tensor of same shape as x.
+    """
+    if any_symbolic_tensors((x,)):
+        return Sinh().symbolic_call(x)
+    return backend.numpy.sinh(x)
+
+
 class Size(Operation):
     def call(self, x):
         return backend.numpy.size(x)
@@ -3376,6 +3496,29 @@ def tan(x):
     return backend.numpy.tan(x)
 
 
+class Tanh(Operation):
+    def call(self, x):
+        return backend.numpy.tanh(x)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=x.dtype)
+
+
+@keras_core_export(["keras_core.ops.tanh", "keras_core.ops.numpy.tanh"])
+def tanh(x):
+    """Hyperbolic tangent, element-wise.
+
+    Arguments:
+        x: Input tensor.
+
+    Returns:
+        Output tensor of same shape as x.
+    """
+    if any_symbolic_tensors((x,)):
+        return Tanh().symbolic_call(x)
+    return backend.numpy.tanh(x)
+
+
 class Tensordot(Operation):
     def __init__(self, axes=2):
         super().__init__()

keras_core/ops/numpy_test.py

@@ -754,14 +754,26 @@ class NumpyOneInputOpsDynamicShapeTest(testing.TestCase):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.arccos(x).shape, (None, 3))
 
+    def test_arccosh(self):
+        x = KerasTensor([None, 3])
+        self.assertEqual(knp.arccosh(x).shape, (None, 3))
+
     def test_arcsin(self):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.arcsin(x).shape, (None, 3))
 
+    def test_arcsinh(self):
+        x = KerasTensor([None, 3])
+        self.assertEqual(knp.arcsinh(x).shape, (None, 3))
+
     def test_arctan(self):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.arctan(x).shape, (None, 3))
 
+    def test_arctanh(self):
+        x = KerasTensor([None, 3])
+        self.assertEqual(knp.arctanh(x).shape, (None, 3))
+
     def test_argmax(self):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.argmax(x).shape, ())
@@ -855,6 +867,10 @@ class NumpyOneInputOpsDynamicShapeTest(testing.TestCase):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.cos(x).shape, (None, 3))
 
+    def test_cosh(self):
+        x = KerasTensor([None, 3])
+        self.assertEqual(knp.cosh(x).shape, (None, 3))
+
     def test_count_nonzero(self):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.count_nonzero(x).shape, ())
@@ -1095,6 +1111,10 @@ class NumpyOneInputOpsDynamicShapeTest(testing.TestCase):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.sin(x).shape, (None, 3))
 
+    def test_sinh(self):
+        x = KerasTensor([None, 3])
+        self.assertEqual(knp.sinh(x).shape, (None, 3))
+
     def test_size(self):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.size(x).shape, ())
@@ -1137,6 +1157,10 @@ class NumpyOneInputOpsDynamicShapeTest(testing.TestCase):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.tan(x).shape, (None, 3))
 
+    def test_tanh(self):
+        x = KerasTensor([None, 3])
+        self.assertEqual(knp.tanh(x).shape, (None, 3))
+
     def test_tile(self):
         x = KerasTensor([None, 3])
         self.assertEqual(knp.tile(x, [2]).shape, (None, 6))
@@ -1227,14 +1251,26 @@ class NumpyOneInputOpsStaticShapeTest(testing.TestCase):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.arccos(x).shape, (2, 3))
 
+    def test_arccosh(self):
+        x = KerasTensor([2, 3])
+        self.assertEqual(knp.arccosh(x).shape, (2, 3))
+
     def test_arcsin(self):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.arcsin(x).shape, (2, 3))
 
+    def test_arcsinh(self):
+        x = KerasTensor([2, 3])
+        self.assertEqual(knp.arcsinh(x).shape, (2, 3))
+
     def test_arctan(self):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.arctan(x).shape, (2, 3))
 
+    def test_arctanh(self):
+        x = KerasTensor([2, 3])
+        self.assertEqual(knp.arctanh(x).shape, (2, 3))
+
     def test_argmax(self):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.argmax(x).shape, ())
@@ -1297,6 +1333,10 @@ class NumpyOneInputOpsStaticShapeTest(testing.TestCase):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.cos(x).shape, (2, 3))
 
+    def test_cosh(self):
+        x = KerasTensor([2, 3])
+        self.assertEqual(knp.cosh(x).shape, (2, 3))
+
     def test_count_nonzero(self):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.count_nonzero(x).shape, ())
@@ -1532,6 +1572,10 @@ class NumpyOneInputOpsStaticShapeTest(testing.TestCase):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.sin(x).shape, (2, 3))
 
+    def test_sinh(self):
+        x = KerasTensor([2, 3])
+        self.assertEqual(knp.sinh(x).shape, (2, 3))
+
     def test_size(self):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.size(x).shape, ())
@@ -1579,6 +1623,10 @@ class NumpyOneInputOpsStaticShapeTest(testing.TestCase):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.tan(x).shape, (2, 3))
 
+    def test_tanh(self):
+        x = KerasTensor([2, 3])
+        self.assertEqual(knp.tanh(x).shape, (2, 3))
+
     def test_tile(self):
         x = KerasTensor([2, 3])
         self.assertEqual(knp.tile(x, [2]).shape, (2, 6))
@@ -2266,18 +2314,42 @@ class NumpyOneInputOpsCorrectnessTest(testing.TestCase):
             np.transpose(x, axes=(1, 0, 3, 2, 4)),
         )
 
-    def test_arcos(self):
+    def test_arccos(self):
         x = np.array([[1, 0.5, -0.7], [0.9, 0.2, -1]])
         self.assertAllClose(knp.arccos(x), np.arccos(x))
 
         self.assertAllClose(knp.Arccos()(x), np.arccos(x))
 
+    def test_arccosh(self):
+        x = np.array([[1, 0.5, -0.7], [0.9, 0.2, -1]])
+        self.assertAllClose(knp.arccosh(x), np.arccosh(x))
+
+        self.assertAllClose(knp.Arccosh()(x), np.arccosh(x))
+
     def test_arcsin(self):
         x = np.array([[1, 0.5, -0.7], [0.9, 0.2, -1]])
         self.assertAllClose(knp.arcsin(x), np.arcsin(x))
 
         self.assertAllClose(knp.Arcsin()(x), np.arcsin(x))
 
+    def test_arcsinh(self):
+        x = np.array([[1, 0.5, -0.7], [0.9, 0.2, -1]])
+        self.assertAllClose(knp.arcsinh(x), np.arcsinh(x))
+
+        self.assertAllClose(knp.Arcsinh()(x), np.arcsinh(x))
+
+    def test_arctan(self):
+        x = np.array([[1, 0.5, -0.7], [0.9, 0.2, -1]])
+        self.assertAllClose(knp.arctan(x), np.arctan(x))
+
+        self.assertAllClose(knp.Arctan()(x), np.arctan(x))
+
+    def test_arctanh(self):
+        x = np.array([[1, 0.5, -0.7], [0.9, 0.2, -1]])
+        self.assertAllClose(knp.arctanh(x), np.arctanh(x))
+
+        self.assertAllClose(knp.Arctanh()(x), np.arctanh(x))
+
     def test_argmax(self):
         x = np.array([[1, 2, 3], [3, 2, 1]])
         self.assertAllClose(knp.argmax(x), np.argmax(x))
@@ -2433,6 +2505,11 @@ class NumpyOneInputOpsCorrectnessTest(testing.TestCase):
         self.assertAllClose(knp.cos(x), np.cos(x))
         self.assertAllClose(knp.Cos()(x), np.cos(x))
 
+    def test_cosh(self):
+        x = np.array([[1, 2, 3], [3, 2, 1]])
+        self.assertAllClose(knp.cosh(x), np.cosh(x))
+        self.assertAllClose(knp.Cosh()(x), np.cosh(x))
+
     def test_count_nonzero(self):
         x = np.array([[0, 2, 3], [3, 2, 0]])
         self.assertAllClose(knp.count_nonzero(x), np.count_nonzero(x))
@@ -2899,6 +2976,11 @@ class NumpyOneInputOpsCorrectnessTest(testing.TestCase):
         self.assertAllClose(knp.sin(x), np.sin(x))
         self.assertAllClose(knp.Sin()(x), np.sin(x))
 
+    def test_sinh(self):
+        x = np.array([[1, -2, 3], [-3, 2, -1]])
+        self.assertAllClose(knp.sinh(x), np.sinh(x))
+        self.assertAllClose(knp.Sinh()(x), np.sinh(x))
+
     def test_size(self):
         x = np.array([[1, 2, 3], [3, 2, 1]])
         self.assertAllClose(knp.size(x), np.size(x))
@@ -2993,6 +3075,11 @@ class NumpyOneInputOpsCorrectnessTest(testing.TestCase):
         self.assertAllClose(knp.tan(x), np.tan(x))
         self.assertAllClose(knp.Tan()(x), np.tan(x))
 
+    def test_tanh(self):
+        x = np.array([[1, -2, 3], [-3, 2, -1]])
+        self.assertAllClose(knp.tanh(x), np.tanh(x))
+        self.assertAllClose(knp.Tanh()(x), np.tanh(x))
+
     def test_tile(self):
         x = np.array([[1, 2, 3], [3, 2, 1]])
         self.assertAllClose(knp.tile(x, [2, 3]), np.tile(x, [2, 3]))